---
title: Naver
---

This notebook covers how to get started with embedding models provided by CLOVA Studio. For detailed documentation on `ClovaXEmbeddings` features and configuration options, please refer to the [API reference](https://guide.ncloud-docs.com/docs/clovastudio-dev-langchain#%EC%9E%84%EB%B2%A0%EB%94%A9%EB%8F%84%EA%B5%AC%EC%9D%B4%EC%9A%A9).

## Overview

### Integration details

| Provider | Package |
|:--------:|:-------:|
| [Naver](/oss/integrations/providers/naver.mdx) | [langchain-naver](https://pypi.org/project/langchain-naver/) |

## Setup

Before using embedding models provided by CLOVA Studio, you must go through the three steps below.

1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account
2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)
3. Create a CLOVA Studio Test App or Service App of a model to use (See [here](https://guide.ncloud-docs.com/docs/clovastudio-explorer03#%ED%85%8C%EC%8A%A4%ED%8A%B8%EC%95%B1%EC%83%9D%EC%84%B1).)
4. Issue a Test or Service API key (See [here](https://guide.ncloud-docs.com/docs/clovastudio-explorer-testapp).)

### Credentials

Set the `CLOVASTUDIO_API_KEY` environment variable with your API key.

```python
import getpass
import os

if not os.getenv("CLOVASTUDIO_API_KEY"):
    os.environ["CLOVASTUDIO_API_KEY"] = getpass.getpass("Enter CLOVA Studio API Key: ")
```

### Installation

ClovaXEmbeddings integration lives in the `langchain_naver` package:

```python
# install package
%pip install -qU langchain-naver
```

## Instantiation

Now we can instantiate our embeddings object and embed query or document:

- There are several embedding models available in CLOVA Studio. Please refer [here](https://guide.ncloud-docs.com/docs/en/clovastudio-explorer03#임베딩API) for further details.
- Note that you might need to normalize the embeddings depending on your specific use case.

```python
from langchain_naver import ClovaXEmbeddings

embeddings = ClovaXEmbeddings(
    model="clir-emb-dolphin"  # set with the model name of corresponding test/service app. Default is `clir-emb-dolphin`
)
```

## Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/oss/langchain/rag).

Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`.

```python
# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "CLOVA Studio is an AI development tool that allows you to customize your own HyperCLOVA X models."

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is CLOVA Studio?")

# show the retrieved document's content
retrieved_documents[0].page_content
```

```output
'CLOVA Studio is an AI development tool that allows you to customize your own HyperCLOVA X models.'
```

## Direct Usage

Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

### Embed single texts

You can embed single texts or documents with `embed_query`:

```python
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector
```

```output
[-0.094717406, -0.4077411, -0.5513184, 1.6024436, -1.3235079, -1.0720996, -0.44471845, 1.3665184, 0.
```

### Embed multiple texts

You can embed multiple texts with `embed_documents`:

```python
text2 = "LangChain is a framework for building context-aware reasoning applications"
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector
```

```output
[-0.094717406, -0.4077411, -0.5513184, 1.6024436, -1.3235079, -1.0720996, -0.44471845, 1.3665184, 0.
[-0.25525448, -0.84877056, -0.6928286, 1.5867524, -1.2930486, -0.8166254, -0.17934391, 1.4236152, 0.
```

## API Reference

For detailed documentation on `ClovaXEmbeddings` features and configuration options, please refer to the [API reference](https://guide.ncloud-docs.com/docs/clovastudio-dev-langchain#%EC%9E%84%EB%B2%A0%EB%94%A9%EB%8F%84%EA%B5%AC%EC%9D%B4%EC%9A%A9).
